Jinhwa Kim


2022

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Why Knowledge Distillation Amplifies Gender Bias and How to Mitigate from the Perspective of DistilBERT
Jaimeen Ahn | Hwaran Lee | Jinhwa Kim | Alice Oh
Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)

Knowledge distillation is widely used to transfer the language understanding of a large model to a smaller model.However, after knowledge distillation, it was found that the smaller model is more biased by gender compared to the source large model.This paper studies what causes gender bias to increase after the knowledge distillation process.Moreover, we suggest applying a variant of the mixup on knowledge distillation, which is used to increase generalizability during the distillation process, not for augmentation.By doing so, we can significantly reduce the gender bias amplification after knowledge distillation.We also conduct an experiment on the GLUE benchmark to demonstrate that even if the mixup is applied, it does not have a significant adverse effect on the model’s performance.

2020

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Analysis of Online Conversations to Detect Cyberpredators Using Recurrent Neural Networks
Jinhwa Kim | Yoon Jo Kim | Mitra Behzadi | Ian G. Harris
Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management

We present an automated approach to analyze the text of an online conversation and determine whether one of the participants is a cyberpredator who is preying on another participant. The task is divided into two stages, 1) the classification of each message, and 2) the classification of the entire conversation. Each stage uses a Recurrent Neural Network (RNN) to perform the classification task.